Authors

Abstract

Particle accelerators are among some of the largest science experiments in the world and can consist of thousands of components with a wide variety of input ranges. These systems can easily become unwieldy optimizationproblems during design and operations studies. Starting in the early 2000s, searching for better beam dynamicsconfigurations became synonymous with heuristic optimization methods in the accelerator physics community.Genetic algorithms and particle swarm optimization are currently the most widely used. These algorithms cantake thousands of simulation evaluations to find optimal solutions for one machine prototype. For large facilitiessuch as the Linac Coherent Light Source (LCLS) and others, this equates to limited exploration of many possibledesign configurations. In this paper, the LCLS-II photoinjector is optimized with three optimization algorithms.All optimizations were started from both a uniform random and Latin hypercube sample. In all cases, theoptimizations started from Latin hypercube samples outperformed optimizations started from uniform samples.All three algorithms were able to optimize the photoinjector, with various trade-offs for each method discussedhere.